completely forgot about gradio

app.py

@@ -1,65 +1,60 @@
-from flask import Flask, request, jsonify
-from flask_cors import CORS
+import gradio as gr
 import os
 import sys
 
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 from api.predict import predict_review, models_loaded
 
-app = Flask(__name__)
-CORS(app)
-
-@app.route('/health', methods=['GET'])
-def health():
-    return jsonify({
-        "status": "ok",
-        "model": "ensemble-v1",
-        "models_loaded": models_loaded
-    }), 200
-
-@app.route('/predict', methods=['POST'])
-def predict():
+def analyze_review(text):
+    if not text or len(text.strip()) == 0:
+        return "error: please enter some text"
+    
+    if not models_loaded:
+        return "models are loading for the first time, this will take 20-30 minutes. please wait..."
+    
     try:
-        data = request.get_json()
-        
-        if not data or 'text' not in data:
-            return jsonify({"error": "missing 'text' field"}), 400
-        
-        reviewText = data['text']
-        
-        if not isinstance(reviewText, str):
-            return jsonify({"error": "'text' must be a string"}), 400
+        result = predict_review(text)
         
-        if len(reviewText.strip()) == 0:
-            return jsonify({"error": "text cannot be empty"}), 400
+        if "error" in result and result["prediction"] == "error":
+            return f"error: {result['error']}"
         
-        if not models_loaded:
-            return jsonify({
-                "status": "loading",
-                "message": "models are loading for the first time, this will take 20-30 minutes. please wait...",
-                "models_loaded": False
-            }), 202
+        prediction = result['prediction']
+        confidence = result['confidence']
+        is_fake = result['is_fake']
         
-        result = predict_review(reviewText)
+        status = "FAKE" if is_fake else "GENUINE"
         
-        if "error" in result and result["prediction"] == "error":
-            return jsonify(result), 400
+        output = f"""prediction: {status}
+confidence: {confidence:.2%}
+
+fake probability: {result['fake_probability']:.2%}
+genuine probability: {result['genuine_probability']:.2%}
+
+model agreement: {result['model_agreement']:.1f}%
+length category: {result['length_category']}
+token count: {result['token_count']}"""
         
-        return jsonify({
-            "prediction": result['prediction'],
-            "confidence": result['confidence'],
-            "is_fake": result['is_fake'],
-            "model_agreement": result['model_agreement'],
-            "fake_probability": result['fake_probability'],
-            "genuine_probability": result['genuine_probability'],
-            "length_category": result['length_category'],
-            "token_count": result['token_count']
-        }), 200
+        return output
         
     except Exception as e:
-        return jsonify({"error": str(e)}), 500
+        return f"error: {str(e)}"
+
+demo = gr.Interface(
+    fn=analyze_review,
+    inputs=gr.Textbox(
+        lines=5,
+        placeholder="paste review text here...",
+        label="review text"
+    ),
+    outputs=gr.Textbox(
+        lines=10,
+        label="analysis"
+    ),
+    title="review classifier",
+    description="ensemble model for detecting fake reviews"
+)
 
-if __name__ == '__main__':
-    print("starting ensemble api server", flush=True)
+if __name__ == "__main__":
+    print("starting gradio interface", flush=True)
     print("models will load on first prediction request", flush=True)
-    app.run(host='0.0.0.0', port=7860, debug=False)
+    demo.launch(server_name="0.0.0.0", server_port=7860)